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Pharmaceutical Manufacturing

AI built for GMP environments that your QP and FDA can both sign off

You get AI agents for batch monitoring, quality control, deviation management, and regulatory documentation — validated to 21 CFR 211 and EU GMP Annex 22, running entirely on your cloud.

or

21 CFR 211 validated. EU GMP Annex 22 compliant. Your cloud. Your data.

Pharmaceutical manufacturing facility
30 days

Proof of concept

12–18 weeks

To measurable ROI

Your cloud

AWS, Azure or GCP

GMP compliance and AI are no longer separate conversations

The FDA's January 2025 AI guidance and the EU's new GMP Annex 22 have made one thing clear: AI systems that influence GMP decisions — batch release, QC, process control — must be validated, documented, and auditable to the same standard as your other controlled systems.

Most AI vendors do not understand GxP. We do. We design every AI workflow to your validation requirements before we build, so your quality team and your regulators see a system they can rely on — not a liability.

21 CFR 211

US GMP validation standard our agents are built to

EU Annex 22

new EU GMP AI standard published for consultation 2025

6 weeks → 1

typical batch investigation time reduction with AI

Zero 483s

on AI documentation for clients entering FDA PAI

Connected

From batch data to GMP-compliant output — in one workflow

We connect your MES, LIMS, ERP, and batch record systems to AI agents that monitor process parameters, detect deviations, accelerate investigations, and generate CAPA documentation — all validated, all auditable, all on your cloud

Manufacturing Data

MES, LIMS, ERP and batch records

AI Agent
ModelYour choice
HostingYour cloud
Workflow Engine
  • Approval gates
  • Human-in-loop
  • Audit logging
Your Cloud Tenant

AWS, Azure or GCP

GMP Output
  • Batch records
  • CAPA documentation
  • Deviation reports
5 Integrations
4 Connections

What we deliver for pharmaceutical manufacturers

GMP-Grade AI

Validated agents built for controlled manufacturing environments

Batch monitoring and release

Monitor CPPs, flag OOS results, generate batch summaries.

ROI

Batch release speed30% faster
Batch failures−25%
Review timeHours vs days

Process

1

Monitor CPPs in real time

Temperature, pressure, pH, and flow data from reactors and process equipment streamed continuously

2

Flag OOS results

AI compares parameters against specification limits and flags deviations requiring investigation

3

Generate batch summaries

Structured batch record summaries compiled for QP review and release decision

Sources

Originally published: AWS Case Study — Pfizer; Azure Case Study — Roche — reproduced for illustrative purposes

Deviation and CAPA management

Classify deviations, suggest root causes, draft CAPA docs.

ROI

Deviation resolution40% faster
Recurring deviations−50%
CAPA effectiveness+35%

Process

1

Classify deviations

AI assesses deviation severity by cross-referencing historical events and batch context

2

Suggest root causes

Pattern matching against past deviations identifies likely root causes and contributing factors

3

Draft CAPA documents

CAPA documentation drafted for quality team review, with supporting evidence attached

Sources

Originally published: GCP Case Study — Novartis; Azure Case Study — GSK — reproduced for illustrative purposes

QC lab automation

Process LIMS results, flag anomalies, escalate for review.

ROI

Drug design speed50% faster
Lab throughput+35%
Anomaly detectionReal-time

Process

1

Process LIMS results

Test results from analytical instruments ingested automatically via LIMS integration

2

Detect trends and anomalies

AI identifies out-of-trend results, batch-to-batch drift, and specification boundary risks

3

Escalate for review

Results requiring scientific judgement escalated with trend context and recommended actions

Sources

Originally published: AWS Case Study — AstraZeneca; Azure Case Study — Merck — reproduced for illustrative purposes

Regulatory document preparation

Structure CTD dossiers and flag issues before FDA/EMA filing.

ROI

Submission assembly60% faster
CTD review time−40%
Error detection99%+

Process

1

Structure dossier content

AI organises study data, CMC sections, and clinical narratives into CTD format

2

Cross-check consistency

Automated checks identify inconsistencies, missing references, and format deviations

3

Prepare for submission

Flagged issues resolved, final dossier assembled for regulatory affairs review

Sources

Originally published: GCP Case Study — Roche; Azure Case Study — Pfizer — reproduced for illustrative purposes

Process analytical technology

Predict product quality from spectroscopic and sensor data.

ROI

Yield improvement20%
Release speed30% faster
Real-time releaseEnabled

Process

1

Capture spectroscopic data

NIR, Raman, and in-line sensor data collected at critical process points in real time

2

Predict quality attributes

Multivariate models predict CQAs from process data, enabling real-time release testing

3

Document and validate

Model predictions logged with validation evidence for QA and regulatory review

Sources

Originally published: AWS Case Study — GSK; Azure Case Study — Novartis — reproduced for illustrative purposes

Supplier and raw material intelligence

Screen supplier quality and flag material risks pre-batch.

ROI

Supplier risk reduction25%
Material rejections−30%
Screening coverage100%

Process

1

Screen supplier data

CoA trends, audit findings, and quality history aggregated across your approved supplier base

2

Score material risk

AI ranks incoming materials by risk based on supplier track record and specification trends

3

Alert before batch impact

High-risk materials flagged for additional testing before they enter the production process

Sources

Originally published: GCP Case Study — J&J; AWS Case Study — AbbVie — reproduced for illustrative purposes

SOP and document control

Find SOP inconsistencies and draft update proposals for QA.

ROI

SOP review speed50% faster
Regulatory change assessment40% faster
Document accuracy99%+

Process

1

Analyse SOP corpus

NLP processes your entire SOP library to map cross-references, definitions, and procedures

2

Detect inconsistencies

AI flags conflicting instructions, outdated references, and gaps against current regulations

3

Propose updates

Draft revisions generated with tracked changes for QA review and document control approval

Sources

Originally published: Azure Case Study — Merck; GCP Case Study — Roche — reproduced for illustrative purposes

Pharmacovigilance and signal detection

Process adverse events and draft ICSRs with human oversight.

ROI

Signal detection speed60% faster
ICSR processing time−45%
Case accuracy98%+

Process

1

Ingest AE reports

Adverse event data from clinical trials, spontaneous reports, and literature collected automatically

2

Classify signal severity

AI scores signal strength, classifies causality, and prioritises cases for medical review

3

Draft ICSRs

Individual case safety reports drafted with human-in-the-loop review before regulatory filing

Sources

Originally published: AWS Case Study — AstraZeneca; Azure Case Study — Novartis — reproduced for illustrative purposes

GMP validation documentation

IQ/OQ/PQ documentation and change control delivered built-in.

ROI

IQ/OQ/PQ speed50% faster
Validation cycle time−35%
Documentation coverage100%

Process

1

Generate IQ/OQ/PQ protocols

Installation, operational, and performance qualification documents created during deployment

2

Execute and document

Validation testing performed with evidence captured in GMP-compliant format

3

Establish periodic review

Change control procedures and scheduled revalidation protocols handed to your QA team

Sources

Originally published: GCP Case Study — Pfizer; AWS Case Study — GSK — reproduced for illustrative purposes

Puja Iyer

Vice President, Digital Innovation, AstraZeneca

Puja Iyer

"By integrating AI into our drug design and optimisation workflows on Microsoft Azure, we have achieved more than 50 percent faster progression from target identification to candidate selection. That is compressing years of traditional research into months — and doing it with the scientific rigour our regulatory submissions demand"

Originally published: Microsoft Customer Stories — AstraZeneca — reproduced for illustrative purposes

01 / 04

How we work in GMP environments

We do not treat GMP compliance as a checkbox after delivery. We map every AI system to your validation requirements, write URS and functional specifications before we build, and deliver IQ/OQ/PQ documentation as part of the engagement.

Every agent runs on your cloud tenant. Your batch data, your LIMS data, your patient data — none of it leaves your environment. Your QP can inspect it. Your FDA investigator can audit it.

01

GMP risk assessment

We classify your AI use case against GxP impact categories and define the validation approach before any build begins.

02

30-day proof of concept

A validated AI agent on your cloud, integrated with your controlled systems, with validation documentation from day one.

03

Validation by design

IQ/OQ/PQ packages, change control procedures, and periodic review protocols delivered alongside the working system.

04

Scale in 12–18 weeks

From one validated workflow to measurable manufacturing efficiency gains — with a compliance record your QP owns.

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